Retrosternal hematoma in sternal fracture for prediction of concomitant injury on chest CT

2021 ◽  
pp. 028418512098157
Author(s):  
Ye Na Son ◽  
Jung Im Kim ◽  
Han Na Lee ◽  
So Youn Shin

Background Isolated sternal fracture, a benign injury, has been increasing in the pan-scan era, although one-third of patients with sternal fracture still has trouble with concomitant injury. The differentiation of these two entities is important to optimize patient management. Purpose To evaluate correlation between retrosternal hematoma and concomitant injury in patients with sternal fracture and to identify predicting factors for concomitant injury in sternal fracture. Material and Methods A total of 139 patients (84 men; mean age = 54.9 ± 15.3 years) with traumatic sternal fracture were enrolled in this study. We reviewed medical charts and multiplanar computed tomography (CT) images to evaluate cause, location, and degree of sternal fracture, retrosternal hematoma, and concomitant injury. Univariate and multivariate analysis were used to identify variables that were associated with concomitant injury. Results Concomitant injury on chest CT was observed in 85 patients with sternal fracture. Of the patients, 98 (70.5%) were accompanied by retrosternal hematoma. Multivariate analysis revealed that retrosternal hematoma (odds ratio [OR] = 5.350; P < 0.001), manubrium fracture (OR = 6.848; P = 0.015), and motor vehicle accident (OR = 0.342; P = 0.015) were significantly associated with sternal fracture with concomitant injury. Conclusion Manubrium fracture and retrosternal hematoma portend a high risk of concomitant injury and indicate the need for further clinical and radiologic work-up.

2003 ◽  
Author(s):  
David Walshe ◽  
Elizabeth Lewis ◽  
Kathleen O'Sullivan ◽  
Brenda K. Wiederhold ◽  
Sun I. Kim

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.


Tracheobronchial foreign bodies are a common problem in clinical practice. We present the case of a patient with three aspirated teeth following a motor vehicle accident.


Author(s):  
Tal Margaliot Kalifa ◽  
Misgav Rottenstreich ◽  
Eyal Mazaki ◽  
Hen Y. Sela ◽  
Schwartz Alon ◽  
...  

2021 ◽  
Author(s):  
Gaia S. Pocobelli ◽  
Mary A. Akosile ◽  
Ryan N. Hansen ◽  
Joanna Eavey ◽  
Robert D. Wellman ◽  
...  

2002 ◽  
Vol 79 (Supplement) ◽  
pp. 91
Author(s):  
Jeffrey Roth ◽  
Khadija Shahid ◽  
Jerome Sherman ◽  
Jeffrey Cooper

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